Skip to main content

Advertisement

SpringerLink
Log in

New trends in precision agriculture: a novel cloud-based system for enabling data storage and agricultural task planning and automation

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

It is well-known that information and communication technologies enable many tasks in the context of precision agriculture. In fact, more and more farmers and food and agriculture companies are using precision agriculture-based systems to enhance not only their products themselves, but also their means of production. Consequently, problems arising from large amounts of data management and processing are arising. It would be very useful to have an infrastructure that allows information and agricultural tasks to be efficiently shared and handled. The cloud computing paradigm offers a solution. In this study, a cloud-based software architecture is proposed with the aim of enabling a complete crop management system to be deployed and validated. Such architecture includes modules developed by using Google App Engine, which allows the information to be easily retrieved and processed and agricultural tasks to be properly defined and planned. Additionally, Google’s Datastore (which ensures a high scalability degree), hosts both information that describes such agricultural tasks and agronomic data. The architecture has been validated in a system that comprises a wireless sensor network with fixed nodes and a mobile node on an unmanned aerial vehicle (UAV), deployed in an agricultural farm in the Region of Murcia (Spain). Such a network allows soil water and plant status to be monitored. The UAV (capable of executing missions defined by an administrator) is useful for acquiring visual information in an autonomous manner (under operator supervision, if needed). The system performance has been analysed and results that demonstrate the benefits of using the proposed architecture are detailed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Balbudhe, K. S., Bulbule, A., Dhanve, N., Raj, S., & Jadhav, N. (2015). Cloud based cultivation management system. ACSIJ Advances in Computer Science: An International Journal, 4, 24–28.

    Google Scholar 

  • Baronti, P., Pillai, P., Chook, V. W., Chessa, S., Gotta, A., & Fu, Y. F. (2007). Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards. Computer Communications, 30, 1655–1695.

    Article  Google Scholar 

  • Buyya, R. (2010). Cloud computing: The next revolution in information technology. In P. Chaudhuri, S. Ghosh, R. Kumar, J. N. Cao & O. Oahiya (Eds.), Proceedings of 1st international conference on parallel distributed and grid computing (PDGC 2010) (pp. 2–3), IEEE, Solan (HP), India. doi:10.1109/PDGC.2010.5679963.

  • Chávez, J. L., Pierce, F. J., Elliott, T. V., & Evans, R. G. (2010). A remote irrigation monitoring and control system for continuous move systems. Part A: Description and development. Precision Agriculture, 11, 1–10. doi:10.1007/s11119-009-9109-1.

    Article  Google Scholar 

  • Da Fonseca, N. L. S. & Boutaba, R. (2015). Cloud architectures, networks, services, and management. Cloud services, networking, and management (Vol. 1). Hoboken, NJ, USA: Wiley. doi: 10.1002/9781119042655.ch1.

  • Eclipse (2017). Integrated development environment. Retrieved April, 2017, from https://eclipse.org/.

  • Geipel, J., Jackenkroll, M., Weis, M., & Claupein, W. (2015). A sensor web-enabled infrastructure for precision farming. ISPRS International Journal of Geo-Information, 4, 385–399. doi:10.3390/ijgi4010385.

    Article  Google Scholar 

  • Gómez-Candón, D., De Castro, A. I., & López-Granados, F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 15, 44–56.

    Article  Google Scholar 

  • Hardt, E. D. (2012). The OAuth 2.0 Authorization Framework. Internet Requests for Comments RFC Editor RFC 6749. Retrieved July, 2017, from http://tools.ietf.org/html/rfc6749.

  • Hvizdoš, J., & Sincák, P. (2015). Control library for AR.Drone 2.0. In IEEE 13th International Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 77–82). doi10.1109/SAMI.2015.7061850

  • Kavis, M. J. (2014). Architecting the cloud: Design decisions for cloud computing service models (SaaS, PaaS, and IaaS). Hoboken, NJ, USA: Wiley. ISBN-13 978-1118617618, ISBN-10 1118617614

  • López, J. A., Garcia-Sanchez, A. J., Soto, F., Iborra, A., Garcia-Sanchez, F., & Garcia-Haro, J. (2011). Design and validation of a wireless sensor network architecture for precision horticulture applications. Precision Agriculture, 12(2), 280–295. doi:10.1007/s11119-010-9178-1.

    Article  Google Scholar 

  • Malawski, M., Kuzniar, M., Wojcik, P., & Bubak, M. (2013). How to use Google App Engine for free computing. IEEE Internet Computing, 17, 50–59. doi:10.1109/MIC.2011.143.

    Article  Google Scholar 

  • Material Design (2017). Description of the material design metaphor. Retrieved April, 2017, from https://material.io/guidelines/.

  • Merino, L., Caballero, F., Martínez de Dios, J. R., Ferruz, J., & Ollero, A. (2006). A cooperative perception system for multiple UAVs: Application to automatic detection of forest fires. Journal of Field Robotics, 23, 65–184.

    Article  Google Scholar 

  • Mondal, P., & Basu, M. (2009). Adoption of precision agriculture technologies in India and in some developing countries: Scope, present status and strategies. Progress in Natural Science, 19, 659–666. doi:10.1016/j.pnsc.2008.07.020.

    Article  Google Scholar 

  • Navarro-Hellín, H., Torres-Sánchez, R., Soto-Valles, F., Albaladejo-Pérez, C., López-Riquelme, J. A., & Domingo-Miguel, R. (2015). A wireless sensors architecture for efficient irrigation water management. Agricultural Water Management, 151, 64–74. doi:10.1016/j.agwat.2014.10.022.

    Article  Google Scholar 

  • Pajares, G. (2015). Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogrammetric Engineering & Remote Sensing, 81, 281–329.

    Article  Google Scholar 

  • Polojärvi, K, Koistinen, M., Luimula, M., Verronen, P., Pahkasalo, M., & Tervonen, J. (2012). Distributed system architectures, standardization, and web-service solutions in precision agriculture. In C. P. Ruckemann (Ed.), Proceedings of the fourth international conference on advanced geographic information systems, applications, and services (GEOProcessing 2012) (pp. 171–176), IARIA, Valencia, Spain.

  • Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., et al. (2009). ROS: An open-source Robot Operating System. In A. Bicchi (Ed.). Proceedings of 2009 IEEE international conference on robotics and automation (ICRA 2009) (pp. 1–6).

  • Reichardt, M., & Jürgens, C. (2009). Adoption and future perspective of precision farming in Germany: Results of several surveys among different agricultural target groups. Precision Agriculture, 10, 73–94. doi:10.1007/s11119-008-9101-1.

    Article  Google Scholar 

  • Sakimura, N., Bradley, J., Jones, M.B., de Medeiros, B., & Mortimore, C. (2014). The OpenID Foundation (OIDF). OpenID Connect Core 1.0. Retrieved July, 2017, from http://openid.net/specs/openid-connect-core-1_0.html.

  • Salamí, E., Barrado, C., & Pastor, E. (2014). UAV flight experiments applied to the remote sensing of vegetated areas. Remote Sensing, 6, 11051–11081. doi:10.3390/rs6111105.

    Article  Google Scholar 

  • Shabani, I., Kovaçi, A., & Dika, A. (2014). Possibilities offered by Google App Engine for developing distributed applications using datastore. In D. Al-Dabass, V. Ameti, F. Skenderi, & F. Halili (Eds.), Proceedings of sixth international conference on computational intelligence, communication systems and networks (CICSyN 2014) (pp. 113–118). IEEE, Tetovo, Macedonia. doi:10.1109/CICSyN.2014.35.

  • Sonka, S. (2014). Big data and the ag sector: More than lots of numbers. International Food and Agribusiness Management Review, 17, 1–20.

    Google Scholar 

  • Tan, L., & Wortman, R. (2014). Cloud-based monitoring and analysis of yield efficiency in precision farming. In IEEE 15th international conference on information reuse and integration (IRI) (pp. 163–170). doi:10.1109/IRI.2014.7051886.

  • Voorsluys, W., Broberg, J., & Buyya, R. (2011). Introduction to cloud computing. In R. Buyya, J. Broberg, & A. Goscinski (Eds.), Cloud computing: Principles and paradigms. Hoboken, NJ, USA: Wiley. doi: 10.1002/9780470940105.ch1.

  • Wang, N., Zhang, N., & Wang, M. (2006). Wireless sensors in agriculture and food industry—Recent development and future perspective. Computers and Electronics in Agriculture, 50, 1–14.

    Article  CAS  Google Scholar 

  • Xiaodong, Z., Lijian, S., Xinhua, J., Seielstad, G., & Helgason, C. (2009). Zone mapping application for precision-farming: a decision support tool for variable rate application. Precision Agriculture, 11, 103–114. doi:10.1007/s11119-009-9130-4.

    Google Scholar 

  • Yesudage, K., Vidyapeeth, B., Bathiya, S., Bora, P., & Waykule, N. (2015). Agro-sense: A mobile app for efficient farming system using Sensors. International Journal of Engineering Research & Technology (IJERT), 4, 456–459.

    Google Scholar 

  • Zahawi, R. A., Dandois, J. P., Holl, K. D., Nadwodny, D., Reid, J. L., & Ellis, E. C. (2015). Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biological Conservation, 186, 287–295. doi:10.1016/j.biocon.2015.03.031.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Hubbard, N., & Loudjani, P. (2014). Precision agriculture: An opportunity for EU farmers-potential support with the CAP 2014-2020. Directorate-General for internal Policies. Policy Department B: Structural And Cohesion Policies. Agriculture and Rural Development. Joint Research Centre (JRC) of the European Commission. Monitoring Agriculture ResourceS (MARS) Unit H04.

  • Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—A worldwide overview. Computers and Electronics in Agriculture, 36, 113–132.

    Article  Google Scholar 

Download references

Acknowledgements

The development of this work was supported by the Spanish Ministry of Science and Innovation through the projects cDrone (ref. TIN2013-45920-R) and RIDEFRUT (ref. AGL2013-49047-C2-1-R). We would like to thank Widhoc Smart Solutions S.L. for allowing us the use of their facilities to carry out the tests. This article is also the result of the activity carried out under the “Research Programme for Groups of Scientific Excellence at Region of Murcia” of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia).

Author information

Authors and Affiliations

  1. University of Huelva, Dr. Cantero Cuadrado, 6, 21071, Huelva, Spain

    N. Pavón-Pulido

  2. Technical University of Cartagena, Campus Muralla del Mar s/n, 30202, Cartagena, Spain

    J. A. López-Riquelme, R. Torres & J. A. Pastor

  3. Pólo INESC-TEC-University of Trás-os-Montes e Alto Douro, Vila Real, Portugal

    R. Morais

Authors
  1. N. Pavón-Pulido
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. A. López-Riquelme
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Torres
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R. Morais
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. J. A. Pastor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. A. López-Riquelme.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pavón-Pulido, N., López-Riquelme, J.A., Torres, R. et al. New trends in precision agriculture: a novel cloud-based system for enabling data storage and agricultural task planning and automation. Precision Agric 18, 1038–1068 (2017). https://doi.org/10.1007/s11119-017-9532-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-017-9532-7

Keywords

Search

Navigation